Novel feature selection approaches for improving the performance of sentiment classification

Abstract

Text based social media has become one of important communication tools between customers and enterprises. In social media, users can easily express their opinions and evaluation regarding products or services. These online user experiences, especially negative evaluations indeed affect other consumers’ behaviors. Consequently, to effectively identify customers’ sentiments and avoid these negative comments to bring a great damage to enterprisers has become one of critical issues. In recent years, machine learning algorithms were viewed as one of effective solutions for sentiment classification. But, when the amount of the online reviews arises, the dimensionality of text data rises remarkably. The performances of machine learning methods have been degraded due to the dimensionality problem. But, conventional feature selection methods tend to select attributes from the majority sentiments, which usually cannot improve classification performance. Therefore, this study attempt to present two feature selection methods called modified categorical proportional difference (MCPD) approach that improves conventional CPD method, and balance category feature (BCF) strategy that equally selects attributes from both positive and negative examples, to improve sentiment classification performances. Finally, several real sentiment cases of text reviews will be provided to demonstrate the effectiveness of our proposed methods. Results showed that the combination of proposed BCF strategy and MCPD method can not only remarkably reduce feature space, but also improve the sentiment classification performance.